Human vs. Machine: Big Data and the Artificial Recommendation Engine

This article was originally posted at SoundCTRL.com on June 11, 2014I’m sure there’s hardly an active internet user today who if asked, would actively elect to use a search return less thorough than that of Google. However, regardless of search engine preference, it’s just “type and hit search”—a quick, easy and reliable set of actions to find the content of the average web surfer.

The more someone searches and surfs, the more the user acquires a stacked history that informs future surfing sessions. This is the case for just about any search query, but when music is specified, is an artificial algorithm’s influence over “what we may like” really helpful in terms of putting new music in front of our eyes and under our mouse cursors? Will we genuinely investigate, listen to, like, and possibly become a full-fledged fan?

Where the general influence and return of search data and music recommendations are concerned, I would say things are more or less enjoyable and helpful depending on where time is spent on the web. Twitter makes recommendations for my accounts, many of whom are artists and bands, which is probably and logically due to my content being music focused. Since overall accounts take priority over the content of tweets, Twitter seems to leave its users a little breathing room to the extent of an algorithm coexisting with a freedom of choice.

You could argue that this is the same with Facebook: regardless of how many suggested/sponsored posts regularly appear on the newsfeed, users either click and listen, or they don’t. However (and this is where algorithms as a general tool can falter and fight among each other) the immediacy of change in what a user sees on Facebook—whether as a post or sidebar ad—makes its recommendation and curation power feel too forced. It feels too inorganic or unnatural to encourage expanding your musical horizons. One wrong click on a Monday surf session and the platform or website in question will for days tell a user they ought to like this person/brand/album/concert, before that data phases out of rotation.

Where I see humans having a continuing edge over general history and data-affected curation is a better inclusion of balance, restraint, and spontaneity. Think of it like the stream of a regular conversation. A chat that starts about a visit to the beach ends in a discussion about the disparity between heavy metal’s lyrical content and nations’ global economic status. If no one explains how things went from one to the other, jumping directly between these topics would lead to abrupt non-sequiturs, incurring confusion or frustration for any onlookers.

Image via Gallo Images/Thinkstock

That’s where data-based curation seems to still lack some finesse. People still have to engage with a link or a streaming file or an artist page in order to learn more and potentially become a fan. But algorithms and anything with a “you may like this” premise can only make suggestions based on what shows up in a database or list from a platform. This makes the presentation of new music very definitive rather than gradual, unlike the natural change of topics in a normal conversation. Algorithms lack the ability to incorporate segues and the incremental transitions to connect those segues. Right now, artificial curation focuses on providing content that has already been engaged, without knowing why, or without factoring in how often.

For even more finely tuned curation/ad suggestions for bands or albums, again, the digital can only go so far into accounting for your preference for “X-band” and the artificial placement of “Y-band.” Perhaps the two outfits are listed under the same genre, BPM, or record label, in front of your eyes and ears. If “X” and “Y” turn out to indeed be similar in genre, the recommendation will feel more organic and likely increase the chances of the newly suggested artist’s music being engaged. Even then, this method of curation relies on a stepping stone mentality and doesn’t account for the spontaneous outliers and blind explorations that humans still like to make and promote, as is evident in rising artist features, blog mixes, or potpourri playlists. (Record of the Day is a great example of this and it is one of my favorite go-to sources for discovering new artists that I end up following and watching develop over time, should their music gain traction.)

Perhaps if artificial and programmed curation is eventually able to negotiate between the “six degrees-esque” suggestion system, and remain unaltered by the incorporation of outlier artist explorations, human curation will be given a stiffer run for its money. Presently, history-based curation will continue to offer a pattern of very similar music, or any content where Facebook is concerned. On the other hand, human-powered curation outlets can choose to take a week to highlight an unexpected album or band without grossly throwing off the expectations of its listeners/readers/followers.

Unless algorithms start surveying users to determine why an individual engaged with a random album of polka favorites on Facebook, Amazon, etc., (and who would even take the time to fill out questions every time they clicked something music related?), in my opinion the bluntness of artificial curation will remain the primary “weakness” of data-sourced curation against human music selection and suggestion.